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license: mit
library_name: keras
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# 🧠 Unet-Brain-Segmentation

A deep learning-based medical image segmentation model for brain MRI scans, built using a TensorFlow implementation of the U-Net architecture.

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## 📌 Model Overview

This model performs **semantic segmentation** on brain MRI images to identify regions such as tumors or anatomical structures. It is based on the **U-Net architecture**, a widely used convolutional neural network for biomedical image segmentation.

### Key Details
- **Model Type:** Image Segmentation (Semantic Segmentation)
- **Architecture:** U-Net
- **Framework:** TensorFlow / Keras
- **Domain:** Medical Imaging (Brain MRI)

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## 🎯 Intended Use

### ✅ Primary Use
- Automatic segmentation of brain MRI images  
- Research in medical imaging and deep learning  
- Educational and experimental purposes  

### ❌ Out-of-Scope Use
- Not intended for clinical diagnosis  
- Should not be used for real-world medical decisions without professional validation  

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## 🏋️ Training Details

### Dataset
- Brain MRI dataset with corresponding segmentation masks  
*(Specify dataset if available, e.g., BraTS or Kaggle Brain MRI dataset)*

### Preprocessing
- Image resizing (e.g., 128×128 or 224×224)  
- Normalization  
- Optional data augmentation (rotations, flips, etc.)  

### Training Configuration
- **Loss Function:** Dice Loss / Binary Cross-Entropy *(update accordingly)*  
- **Optimizer:** Adam  
- **Batch Size:** *(add your value)*  
- **Epochs:** *(add your value)*  

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## 🧠 Model Architecture

The model follows the classic **U-Net encoder–decoder structure**:

- **Encoder:** Extracts hierarchical features from input images  
- **Decoder:** Upsamples features to generate segmentation masks  
- **Skip Connections:** Preserve spatial information and improve localization  

This design enables **precise pixel-level predictions**, which are essential for medical image analysis.

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## 📊 Evaluation

### Metrics
- Dice Coefficient  
- Intersection over Union (IoU)  

### Example Results